Sleep stage performs a vital role in people's daily lives in the detection of sleep-related diseases. Conventional automated sleep stage classifier models are not efficient due to the complexity linked to the design of mathematical models and extraction of hand-engineering features. Further, quick oscillations amongst sleep stages frequently lead to indistinct feature extraction, which might result in the imprecise classification of sleep stages. To resolve these issues, deep learning (DL) models are applied, which make use of many layers of linear and nonlinear processing components for learning the hierarchical representation or feature from input data and have been used for sleep stage classification (SSC). Therefore, this paper proposes an ensemble of voting-based DL models, namely the recurrent neural network (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU), with activation Regularization (AR) functions for SSC. The penalty addition of L1, L1_L2, and L2 on the layers of the model fine-tunes it in proportion to the magnitude of the activation function in the model by reducing overfitting. Subsequently, the presented model integrates the results of every classification model to the max voting combination rule. Finally, experimental results of the proposed approach using the benchmark Sleep Stage dataset are evaluated using various metrics. The experimental results illustrates that the Ensemble RNN, Ensemble GRU, and Ensemble LSTM models have achieved an accuracy of 85.57%, 87.41%, and 89.01%, respectively.
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